Recently, many novel neural networks were designed to extract discriminative representations for AMC in order to improve the classification performance. A long short-term memory (LSTM) based AMC algorithm was proposed in [1]} to identify modulation formats, but its recurrent structure results in high computational complexity. Inspired by the residual learning for image classification, a modified residual network (ResNet) was applied to extract features from the received I/Q symbols for AMC [2]}. However, the classification performance is limit due to the over-fitting problem caused by a large number of network parameters [3]}. Besides those networks, other promising techniques were also used for advancing the performance. For example, a graph convolutional network was investigated for AMC algorithm in [4]}. These novel neural networks have improved the classification performance significantly. However, the performance is still limit due to the complex environments, and deep networks need a longer time to converge. In the beyond fifth generation (5G wireless communication networks), intelligent communications with high reliability and low latency are the main characteristics. However, the traditional AMC schemes based on convolution cannot satisfy the classification performance and low computing cost requirements of the beyond 5G wireless communication networks.
